Learn Azure AI Search with C# — index data, run vector and hybrid queries, and add RAG to your .NET app. Start building intelligent search today. If your application's search box still runs a LIKE '%term%' query against SQL Server, your users are quietly suffering. They type "cheap laptop for uni" and get zero results because your catalogue says "affordable notebook for students." Azure AI Search with C# fixes exactly this problem: it combines classic keyword search, vector embeddings, and semantic reranking into a single managed service that you can drive from .NET with a few dozen lines of code. In this tutorial you'll build a working search index from scratch, run keyword, vector, and hybrid queries, and finish with a Retrieval Augmented Generation (RAG) pattern that grounds an LLM in your own data. This guide targets .NET 9 and the Azure.Search.Documents v11 SDK. Every snippet is runnable. We'll explain why each design choice matter...
Learn how to run background jobs in .NET with Hangfire, Quartz.NET, and IHostedService. Compare features, see code examples, and pick the right tool. Sooner or later, every ASP.NET Core application needs to do work outside the request pipeline: sending emails, generating reports, syncing data with a third-party API, or cleaning up stale records at 2 AM. That's when developers start searching for how to run background jobs in .NET — and immediately run into three popular options: Hangfire , IHostedService (with its BackgroundService base class), and Quartz.NET . All three are production-proven. All three can run a scheduled task. But they solve different problems, and picking the wrong one is a common source of pain: lost jobs after a deploy, duplicate work when you scale to two servers, or hundreds of lines of hand-rolled retry logic that a library would have given you for free. In this guide we'll build the same job with each approach, compare them honestly, and give ...